def empty_like(space, w_a, w_dtype=None, w_order=None, subok=True):
w_a = convert_to_array(space, w_a)
npy_order = order_converter(space, w_order, w_a.get_order())
if space.is_none(w_dtype):
dtype = w_a.get_dtype()
else:
dtype = space.interp_w(
descriptor.W_Dtype,
space.call_function(space.gettypefor(descriptor.W_Dtype), w_dtype))
if dtype.is_str_or_unicode() and dtype.elsize < 1:
dtype = descriptor.variable_dtype(space, dtype.char + '1')
if npy_order in (NPY.KEEPORDER, NPY.ANYORDER):
# Try to copy the stride pattern
impl = w_a.implementation.astype(space, dtype, NPY.KEEPORDER)
if subok:
w_type = space.type(w_a)
else:
w_type = None
return wrap_impl(space, w_type, w_a, impl)
return W_NDimArray.from_shape(space,
w_a.get_shape(),
dtype=dtype,
order=npy_order,
w_instance=w_a if subok else None,
zero=False)
def descr_new_nditer(
space, w_subtype, w_seq, w_flags, w_op_flags, w_op_dtypes, w_casting, w_op_axes, w_itershape, w_buffersize, w_order
):
npy_order = order_converter(space, w_order, NPY.KEEPORDER)
buffersize = space.int_w(w_buffersize)
return W_NDIter(
space, w_seq, w_flags, w_op_flags, w_op_dtypes, w_casting, w_op_axes, w_itershape, buffersize, npy_order
)
def _zeros_or_empty(space, w_shape, w_dtype, w_order, zero):
# w_order can be None, str, or boolean
order = order_converter(space, w_order, NPY.CORDER)
dtype = space.interp_w(descriptor.W_Dtype,
space.call_function(space.gettypefor(descriptor.W_Dtype), w_dtype))
if dtype.is_str_or_unicode() and dtype.elsize < 1:
dtype = descriptor.variable_dtype(space, dtype.char + '1')
shape = shape_converter(space, w_shape, dtype)
for dim in shape:
if dim < 0:
raise oefmt(space.w_ValueError,
"negative dimensions are not allowed")
try:
support.product_check(shape)
except OverflowError:
raise oefmt(space.w_ValueError, "array is too big.")
return W_NDimArray.from_shape(space, shape, dtype, order, zero=zero)
def _zeros_or_empty(space, w_shape, w_dtype, w_order, zero):
# w_order can be None, str, or boolean
order = order_converter(space, w_order, NPY.CORDER)
dtype = space.interp_w(descriptor.W_Dtype,
space.call_function(space.gettypefor(descriptor.W_Dtype), w_dtype))
if dtype.is_str_or_unicode() and dtype.elsize < 1:
dtype = descriptor.variable_dtype(space, dtype.char + '1')
shape = shape_converter(space, w_shape, dtype)
for dim in shape:
if dim < 0:
raise OperationError(space.w_ValueError, space.wrap(
"negative dimensions are not allowed"))
try:
support.product_check(shape)
except OverflowError:
raise oefmt(space.w_ValueError, "array is too big.")
return W_NDimArray.from_shape(space, shape, dtype, order, zero=zero)
def empty_like(space, w_a, w_dtype=None, w_order=None, subok=True):
w_a = convert_to_array(space, w_a)
npy_order = order_converter(space, w_order, w_a.get_order())
if space.is_none(w_dtype):
dtype = w_a.get_dtype()
else:
dtype = space.interp_w(descriptor.W_Dtype,
space.call_function(space.gettypefor(descriptor.W_Dtype), w_dtype))
if dtype.is_str_or_unicode() and dtype.elsize < 1:
dtype = descriptor.variable_dtype(space, dtype.char + '1')
if npy_order in (NPY.KEEPORDER, NPY.ANYORDER):
# Try to copy the stride pattern
impl = w_a.implementation.astype(space, dtype, NPY.KEEPORDER)
if subok:
w_type = space.type(w_a)
else:
w_type = None
return wrap_impl(space, w_type, w_a, impl)
return W_NDimArray.from_shape(space, w_a.get_shape(), dtype=dtype,
order=npy_order,
w_instance=w_a if subok else None,
zero=False)
def descr_new_nditer(space, w_subtype, w_seq, w_flags, w_op_flags, w_op_dtypes,
w_casting, w_op_axes, w_itershape, w_buffersize, w_order):
npy_order = order_converter(space, w_order, NPY.KEEPORDER)
buffersize = space.int_w(w_buffersize)
return W_NDIter(space, w_seq, w_flags, w_op_flags, w_op_dtypes, w_casting,
w_op_axes, w_itershape, buffersize, npy_order)
def _array(space,
w_object,
w_dtype=None,
copy=True,
w_order=None,
subok=False):
from pypy.module.micronumpy.boxes import W_GenericBox
# numpy testing calls array(type(array([]))) and expects a ValueError
if space.isinstance_w(w_object, space.w_type):
raise oefmt(space.w_ValueError,
"cannot create ndarray from type instance")
# for anything that isn't already an array, try __array__ method first
dtype = descriptor.decode_w_dtype(space, w_dtype)
if not isinstance(w_object, W_NDimArray):
w_array = try_array_method(space, w_object, w_dtype)
if w_array is None:
if (not space.isinstance_w(w_object, space.w_bytes)
and not space.isinstance_w(w_object, space.w_unicode)
and not isinstance(w_object, W_GenericBox)):
# use buffer interface
w_object = _array_from_buffer_3118(space, w_object, dtype)
else:
# continue with w_array, but do further operations in place
w_object = w_array
copy = False
dtype = w_object.get_dtype()
if not isinstance(w_object, W_NDimArray):
w_array, _copy = try_interface_method(space, w_object, copy)
if w_array is not None:
w_object = w_array
copy = _copy
dtype = w_object.get_dtype()
if isinstance(w_object, W_NDimArray):
npy_order = order_converter(space, w_order, NPY.ANYORDER)
if (dtype is None or w_object.get_dtype() is dtype) and (
subok or type(w_object) is W_NDimArray):
flags = w_object.get_flags()
must_copy = copy
must_copy |= (npy_order == NPY.CORDER
and not flags & NPY.ARRAY_C_CONTIGUOUS)
must_copy |= (npy_order == NPY.FORTRANORDER
and not flags & NPY.ARRAY_F_CONTIGUOUS)
if must_copy:
return w_object.descr_copy(space, space.newint(npy_order))
else:
return w_object
if subok and not type(w_object) is W_NDimArray:
raise oefmt(space.w_NotImplementedError,
"array(..., subok=True) only partially implemented")
# we have a ndarray, but need to copy or change dtype
if dtype is None:
dtype = w_object.get_dtype()
if dtype != w_object.get_dtype():
# silently reject the copy value
copy = True
if copy:
shape = w_object.get_shape()
order = support.get_order_as_CF(w_object.get_order(), npy_order)
w_arr = W_NDimArray.from_shape(space, shape, dtype, order=order)
if support.product(shape) == 1:
w_arr.set_scalar_value(
dtype.coerce(space, w_object.implementation.getitem(0)))
else:
loop.setslice(space, shape, w_arr.implementation,
w_object.implementation)
return w_arr
else:
imp = w_object.implementation
w_base = w_object
sz = w_base.get_size() * dtype.elsize
if imp.base() is not None:
w_base = imp.base()
if type(w_base) is W_NDimArray:
sz = w_base.get_size() * dtype.elsize
else:
# this must succeed (mmap, buffer, ...)
sz = space.int_w(space.call_method(w_base, 'size'))
with imp as storage:
return W_NDimArray.from_shape_and_storage(space,
w_object.get_shape(),
storage,
dtype,
storage_bytes=sz,
w_base=w_base,
strides=imp.strides,
start=imp.start)
else:
# not an array
npy_order = order_converter(space, w_order, NPY.CORDER)
shape, elems_w = find_shape_and_elems(space, w_object, dtype)
if dtype is None and space.isinstance_w(w_object, space.w_buffer):
dtype = descriptor.get_dtype_cache(space).w_uint8dtype
if dtype is None or (dtype.is_str_or_unicode() and dtype.elsize < 1):
dtype = find_dtype_for_seq(space, elems_w, dtype)
w_arr = W_NDimArray.from_shape(space, shape, dtype, order=npy_order)
if support.product(
shape) == 1: # safe from overflow since from_shape checks
w_arr.set_scalar_value(dtype.coerce(space, elems_w[0]))
else:
loop.assign(space, w_arr, elems_w)
return w_arr
def _array(space, w_object, w_dtype=None, copy=True, w_order=None, subok=False):
from pypy.module.micronumpy.boxes import W_GenericBox
# numpy testing calls array(type(array([]))) and expects a ValueError
if space.isinstance_w(w_object, space.w_type):
raise oefmt(space.w_ValueError, "cannot create ndarray from type instance")
# for anything that isn't already an array, try __array__ method first
dtype = descriptor.decode_w_dtype(space, w_dtype)
if not isinstance(w_object, W_NDimArray):
w_array = try_array_method(space, w_object, w_dtype)
if w_array is None:
if ( not space.isinstance_w(w_object, space.w_str) and
not space.isinstance_w(w_object, space.w_unicode) and
not isinstance(w_object, W_GenericBox)):
# use buffer interface
w_object = _array_from_buffer_3118(space, w_object, dtype)
else:
# continue with w_array, but do further operations in place
w_object = w_array
copy = False
dtype = w_object.get_dtype()
if not isinstance(w_object, W_NDimArray):
w_array, _copy = try_interface_method(space, w_object, copy)
if w_array is not None:
w_object = w_array
copy = _copy
dtype = w_object.get_dtype()
if isinstance(w_object, W_NDimArray):
npy_order = order_converter(space, w_order, NPY.ANYORDER)
if (dtype is None or w_object.get_dtype() is dtype) and (subok or
type(w_object) is W_NDimArray):
flags = w_object.get_flags()
must_copy = copy
must_copy |= (npy_order == NPY.CORDER and not flags & NPY.ARRAY_C_CONTIGUOUS)
must_copy |= (npy_order == NPY.FORTRANORDER and not flags & NPY.ARRAY_F_CONTIGUOUS)
if must_copy:
return w_object.descr_copy(space, space.wrap(npy_order))
else:
return w_object
if subok and not type(w_object) is W_NDimArray:
raise oefmt(space.w_NotImplementedError,
"array(..., subok=True) only partially implemented")
# we have a ndarray, but need to copy or change dtype
if dtype is None:
dtype = w_object.get_dtype()
if dtype != w_object.get_dtype():
# silently reject the copy value
copy = True
if copy:
shape = w_object.get_shape()
order = support.get_order_as_CF(w_object.get_order(), npy_order)
w_arr = W_NDimArray.from_shape(space, shape, dtype, order=order)
if support.product(shape) == 1:
w_arr.set_scalar_value(dtype.coerce(space,
w_object.implementation.getitem(0)))
else:
loop.setslice(space, shape, w_arr.implementation, w_object.implementation)
return w_arr
else:
imp = w_object.implementation
w_base = w_object
sz = w_base.get_size() * dtype.elsize
if imp.base() is not None:
w_base = imp.base()
if type(w_base) is W_NDimArray:
sz = w_base.get_size() * dtype.elsize
else:
# this must succeed (mmap, buffer, ...)
sz = space.int_w(space.call_method(w_base, 'size'))
with imp as storage:
return W_NDimArray.from_shape_and_storage(space,
w_object.get_shape(), storage, dtype, storage_bytes=sz,
w_base=w_base, strides=imp.strides, start=imp.start)
else:
# not an array
npy_order = order_converter(space, w_order, NPY.CORDER)
shape, elems_w = find_shape_and_elems(space, w_object, dtype)
if dtype is None and space.isinstance_w(w_object, space.w_buffer):
dtype = descriptor.get_dtype_cache(space).w_uint8dtype
if dtype is None or (dtype.is_str_or_unicode() and dtype.elsize < 1):
dtype = find_dtype_for_seq(space, elems_w, dtype)
w_arr = W_NDimArray.from_shape(space, shape, dtype, order=npy_order)
if support.product(shape) == 1: # safe from overflow since from_shape checks
w_arr.set_scalar_value(dtype.coerce(space, elems_w[0]))
else:
loop.assign(space, w_arr, elems_w)
return w_arr
